Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations7739
Missing cells227
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory587.9 B

Variable types

Numeric17
Categorical9

Alerts

aps is highly overall correlated with avtisst and 2 other fieldsHigh correlation
avtisst is highly overall correlated with aps and 2 other fieldsHigh correlation
ca is highly overall correlated with dzclass and 1 other fieldsHigh correlation
dnr is highly overall correlated with hospdeadHigh correlation
dzclass is highly overall correlated with ca and 1 other fieldsHigh correlation
dzgroup is highly overall correlated with ca and 1 other fieldsHigh correlation
hospdead is highly overall correlated with avtisst and 2 other fieldsHigh correlation
scoma is highly overall correlated with surv2mHigh correlation
sps is highly overall correlated with aps and 3 other fieldsHigh correlation
surv2m is highly overall correlated with aps and 4 other fieldsHigh correlation
surv6m is highly overall correlated with sps and 1 other fieldsHigh correlation
race is highly imbalanced (58.4%)Imbalance
dementia is highly imbalanced (79.1%)Imbalance
num.co has 1005 (13.0%) zerosZeros
scoma has 5435 (70.2%) zerosZeros
surv2m has 120 (1.6%) zerosZeros
surv6m has 174 (2.2%) zerosZeros
adlsc has 2649 (34.2%) zerosZeros

Reproduction

Analysis started2024-09-24 15:41:29.841678
Analysis finished2024-09-24 15:41:43.063038
Duration13.22 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct6425
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.670646
Minimum18.04199
Maximum101.84796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:43.090739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18.04199
5-th percentile33.16299
Q152.940475
median64.87598
Q374.087495
95-th percentile85.130987
Maximum101.84796
Range83.80597
Interquartile range (IQR)21.14702

Descriptive statistics

Standard deviation15.624771
Coefficient of variation (CV)0.24931562
Kurtosis-0.15509917
Mean62.670646
Median Absolute Deviation (MAD)10.31598
Skewness-0.50854255
Sum485008.13
Variance244.13346
MonotonicityNot monotonic
2024-09-24T10:41:43.135353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.28699 4
 
0.1%
52.98599 4
 
0.1%
69.125 4
 
0.1%
71.461 4
 
0.1%
74.46698 4
 
0.1%
71.99695 4
 
0.1%
69.54095 4
 
0.1%
65.53894 4
 
0.1%
55.42499 4
 
0.1%
68.64099 4
 
0.1%
Other values (6415) 7699
99.5%
ValueCountFrequency (%)
18.04199 1
< 0.1%
18.146 1
< 0.1%
18.16299 1
< 0.1%
18.18999 1
< 0.1%
18.41499 1
< 0.1%
18.502 1
< 0.1%
18.754 1
< 0.1%
18.77599 1
< 0.1%
18.84999 1
< 0.1%
18.946 1
< 0.1%
ValueCountFrequency (%)
101.84796 1
< 0.1%
100.849 1
< 0.1%
100.67896 1
< 0.1%
100.24597 1
< 0.1%
100.13098 1
< 0.1%
99.28497 1
< 0.1%
98.08899 1
< 0.1%
97.83997 1
< 0.1%
97.59595 1
< 0.1%
97.51099 1
< 0.1%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size786.1 KiB
male
4390 
female
3349 

Length

Max length6
Median length4
Mean length4.8654865
Min length4

Characters and Unicode

Total characters37654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
male 4390
56.7%
female 3349
43.3%

Length

2024-09-24T10:41:43.176587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T10:41:43.224709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 4390
56.7%
female 3349
43.3%

Most occurring characters

ValueCountFrequency (%)
e 11088
29.4%
m 7739
20.6%
a 7739
20.6%
l 7739
20.6%
f 3349
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11088
29.4%
m 7739
20.6%
a 7739
20.6%
l 7739
20.6%
f 3349
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11088
29.4%
m 7739
20.6%
a 7739
20.6%
l 7739
20.6%
f 3349
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11088
29.4%
m 7739
20.6%
a 7739
20.6%
l 7739
20.6%
f 3349
 
8.9%

dzgroup
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size831.7 KiB
ARF/MOSF w/Sepsis
2971 
CHF
1173 
COPD
823 
Lung Cancer
782 
MOSF w/Malig
611 
Other values (3)
1379 

Length

Max length17
Median length12
Mean length10.906189
Min length3

Characters and Unicode

Total characters84403
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMOSF w/Malig
2nd rowARF/MOSF w/Sepsis
3rd rowCHF
4th rowCirrhosis
5th rowCOPD

Common Values

ValueCountFrequency (%)
ARF/MOSF w/Sepsis 2971
38.4%
CHF 1173
 
15.2%
COPD 823
 
10.6%
Lung Cancer 782
 
10.1%
MOSF w/Malig 611
 
7.9%
Coma 510
 
6.6%
Cirrhosis 439
 
5.7%
Colon Cancer 430
 
5.6%

Length

2024-09-24T10:41:43.259512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T10:41:43.305836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
arf/mosf 2971
23.7%
w/sepsis 2971
23.7%
cancer 1212
9.7%
chf 1173
 
9.4%
copd 823
 
6.6%
lung 782
 
6.2%
mosf 611
 
4.9%
w/malig 611
 
4.9%
coma 510
 
4.1%
cirrhosis 439
 
3.5%

Most occurring characters

ValueCountFrequency (%)
F 7726
 
9.2%
s 6820
 
8.1%
/ 6553
 
7.8%
S 6553
 
7.8%
4794
 
5.7%
C 4587
 
5.4%
i 4460
 
5.3%
O 4405
 
5.2%
M 4193
 
5.0%
e 4183
 
5.0%
Other values (18) 30129
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 7726
 
9.2%
s 6820
 
8.1%
/ 6553
 
7.8%
S 6553
 
7.8%
4794
 
5.7%
C 4587
 
5.4%
i 4460
 
5.3%
O 4405
 
5.2%
M 4193
 
5.0%
e 4183
 
5.0%
Other values (18) 30129
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 7726
 
9.2%
s 6820
 
8.1%
/ 6553
 
7.8%
S 6553
 
7.8%
4794
 
5.7%
C 4587
 
5.4%
i 4460
 
5.3%
O 4405
 
5.2%
M 4193
 
5.0%
e 4183
 
5.0%
Other values (18) 30129
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 7726
 
9.2%
s 6820
 
8.1%
/ 6553
 
7.8%
S 6553
 
7.8%
4794
 
5.7%
C 4587
 
5.4%
i 4460
 
5.3%
O 4405
 
5.2%
M 4193
 
5.0%
e 4183
 
5.0%
Other values (18) 30129
35.7%

dzclass
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size829.2 KiB
ARF/MOSF
3582 
COPD/CHF/Cirrhosis
2435 
Cancer
1212 
Coma
510 

Length

Max length18
Median length8
Mean length10.569583
Min length4

Characters and Unicode

Total characters81798
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARF/MOSF
2nd rowARF/MOSF
3rd rowCOPD/CHF/Cirrhosis
4th rowCOPD/CHF/Cirrhosis
5th rowCOPD/CHF/Cirrhosis

Common Values

ValueCountFrequency (%)
ARF/MOSF 3582
46.3%
COPD/CHF/Cirrhosis 2435
31.5%
Cancer 1212
 
15.7%
Coma 510
 
6.6%

Length

2024-09-24T10:41:43.353105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T10:41:43.396329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
arf/mosf 3582
46.3%
copd/chf/cirrhosis 2435
31.5%
cancer 1212
 
15.7%
coma 510
 
6.6%

Most occurring characters

ValueCountFrequency (%)
F 9599
11.7%
C 9027
11.0%
/ 8452
 
10.3%
r 6082
 
7.4%
O 6017
 
7.4%
s 4870
 
6.0%
i 4870
 
6.0%
A 3582
 
4.4%
M 3582
 
4.4%
S 3582
 
4.4%
Other values (11) 22135
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 9599
11.7%
C 9027
11.0%
/ 8452
 
10.3%
r 6082
 
7.4%
O 6017
 
7.4%
s 4870
 
6.0%
i 4870
 
6.0%
A 3582
 
4.4%
M 3582
 
4.4%
S 3582
 
4.4%
Other values (11) 22135
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 9599
11.7%
C 9027
11.0%
/ 8452
 
10.3%
r 6082
 
7.4%
O 6017
 
7.4%
s 4870
 
6.0%
i 4870
 
6.0%
A 3582
 
4.4%
M 3582
 
4.4%
S 3582
 
4.4%
Other values (11) 22135
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 9599
11.7%
C 9027
11.0%
/ 8452
 
10.3%
r 6082
 
7.4%
O 6017
 
7.4%
s 4870
 
6.0%
i 4870
 
6.0%
A 3582
 
4.4%
M 3582
 
4.4%
S 3582
 
4.4%
Other values (11) 22135
27.1%

num.co
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8702675
Minimum0
Maximum9
Zeros1005
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:43.430819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3516858
Coefficient of variation (CV)0.72272328
Kurtosis0.66788723
Mean1.8702675
Median Absolute Deviation (MAD)1
Skewness0.83493303
Sum14474
Variance1.8270546
MonotonicityNot monotonic
2024-09-24T10:41:43.461584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 2561
33.1%
2 2024
26.2%
3 1204
15.6%
0 1005
 
13.0%
4 608
 
7.9%
5 228
 
2.9%
6 87
 
1.1%
7 18
 
0.2%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 1005
 
13.0%
1 2561
33.1%
2 2024
26.2%
3 1204
15.6%
4 608
 
7.9%
5 228
 
2.9%
6 87
 
1.1%
7 18
 
0.2%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 3
 
< 0.1%
7 18
 
0.2%
6 87
 
1.1%
5 228
 
2.9%
4 608
 
7.9%
3 1204
15.6%
2 2024
26.2%
1 2561
33.1%
0 1005
 
13.0%

scoma
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.07741
Minimum0
Maximum100
Zeros5435
Zeros (%)70.2%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:43.494402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile61
Maximum100
Range100
Interquartile range (IQR)9

Descriptive statistics

Standard deviation24.670994
Coefficient of variation (CV)2.0427388
Kurtosis4.8536201
Mean12.07741
Median Absolute Deviation (MAD)0
Skewness2.3349461
Sum93455
Variance608.65796
MonotonicityNot monotonic
2024-09-24T10:41:43.527656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 5435
70.2%
9 643
 
8.3%
26 364
 
4.7%
44 333
 
4.3%
100 287
 
3.7%
37 222
 
2.9%
41 173
 
2.2%
61 105
 
1.4%
55 81
 
1.0%
89 49
 
0.6%
ValueCountFrequency (%)
0 5435
70.2%
9 643
 
8.3%
26 364
 
4.7%
37 222
 
2.9%
41 173
 
2.2%
44 333
 
4.3%
55 81
 
1.0%
61 105
 
1.4%
89 49
 
0.6%
94 46
 
0.6%
ValueCountFrequency (%)
100 287
3.7%
94 46
 
0.6%
89 49
 
0.6%
61 105
 
1.4%
55 81
 
1.0%
44 333
4.3%
41 173
 
2.2%
37 222
 
2.9%
26 364
4.7%
9 643
8.3%

avtisst
Real number (ℝ)

HIGH CORRELATION 

Distinct348
Distinct (%)4.5%
Missing71
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean22.565824
Minimum1
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:43.568946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q112
median19.5
Q331.541664
95-th percentile48
Maximum83
Range82
Interquartile range (IQR)19.541664

Descriptive statistics

Standard deviation13.264845
Coefficient of variation (CV)0.58782894
Kurtosis-0.063614371
Mean22.565824
Median Absolute Deviation (MAD)9
Skewness0.77219709
Sum173034.74
Variance175.9561
MonotonicityNot monotonic
2024-09-24T10:41:43.614904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 208
 
2.7%
10 195
 
2.5%
12 191
 
2.5%
9 185
 
2.4%
13 184
 
2.4%
8 183
 
2.4%
7 165
 
2.1%
16 158
 
2.0%
14 151
 
2.0%
15 145
 
1.9%
Other values (338) 5903
76.3%
ValueCountFrequency (%)
1 5
 
0.1%
1.5 1
 
< 0.1%
1.666666 1
 
< 0.1%
2 9
 
0.1%
2.333332 1
 
< 0.1%
2.5 8
 
0.1%
3 32
0.4%
3.25 1
 
< 0.1%
3.333332 1
 
< 0.1%
3.5 12
 
0.2%
ValueCountFrequency (%)
83 1
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
75 1
< 0.1%
73 1
< 0.1%
72 1
< 0.1%
70 2
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%

race
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing35
Missing (%)0.5%
Memory size786.8 KiB
white
6122 
black
1189 
hispanic
 
244
other
 
90
asian
 
59

Length

Max length8
Median length5
Mean length5.0950156
Min length5

Characters and Unicode

Total characters39252
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowhispanic
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 6122
79.1%
black 1189
 
15.4%
hispanic 244
 
3.2%
other 90
 
1.2%
asian 59
 
0.8%
(Missing) 35
 
0.5%

Length

2024-09-24T10:41:43.660286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T10:41:43.704494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
white 6122
79.5%
black 1189
 
15.4%
hispanic 244
 
3.2%
other 90
 
1.2%
asian 59
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i 6669
17.0%
h 6456
16.4%
t 6212
15.8%
e 6212
15.8%
w 6122
15.6%
a 1551
 
4.0%
c 1433
 
3.7%
b 1189
 
3.0%
l 1189
 
3.0%
k 1189
 
3.0%
Other values (5) 1030
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6669
17.0%
h 6456
16.4%
t 6212
15.8%
e 6212
15.8%
w 6122
15.6%
a 1551
 
4.0%
c 1433
 
3.7%
b 1189
 
3.0%
l 1189
 
3.0%
k 1189
 
3.0%
Other values (5) 1030
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6669
17.0%
h 6456
16.4%
t 6212
15.8%
e 6212
15.8%
w 6122
15.6%
a 1551
 
4.0%
c 1433
 
3.7%
b 1189
 
3.0%
l 1189
 
3.0%
k 1189
 
3.0%
Other values (5) 1030
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6669
17.0%
h 6456
16.4%
t 6212
15.8%
e 6212
15.8%
w 6122
15.6%
a 1551
 
4.0%
c 1433
 
3.7%
b 1189
 
3.0%
l 1189
 
3.0%
k 1189
 
3.0%
Other values (5) 1030
 
2.6%

sps
Real number (ℝ)

HIGH CORRELATION 

Distinct585
Distinct (%)7.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean25.526894
Minimum0.1999817
Maximum99.1875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:43.746501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1999817
5-th percentile13.699219
Q119
median23.898438
Q330.199219
95-th percentile42.710547
Maximum99.1875
Range98.987518
Interquartile range (IQR)11.199219

Descriptive statistics

Standard deviation9.8825634
Coefficient of variation (CV)0.3871432
Kurtosis5.5660309
Mean25.526894
Median Absolute Deviation (MAD)5.5
Skewness1.5852019
Sum197527.11
Variance97.665059
MonotonicityNot monotonic
2024-09-24T10:41:43.790954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.8984375 57
 
0.7%
22.6992188 52
 
0.7%
20.6992188 51
 
0.7%
21.0976562 50
 
0.6%
16.796875 49
 
0.6%
23.5976562 49
 
0.6%
21.6992188 49
 
0.6%
21.8984375 49
 
0.6%
21.5976562 47
 
0.6%
19.8984375 46
 
0.6%
Other values (575) 7239
93.5%
ValueCountFrequency (%)
0.1999817 1
 
< 0.1%
1.1999512 2
< 0.1%
1.5 1
 
< 0.1%
1.8999023 1
 
< 0.1%
2 2
< 0.1%
2.2998047 1
 
< 0.1%
2.699707 1
 
< 0.1%
3 1
 
< 0.1%
3.0996094 1
 
< 0.1%
3.2998047 3
< 0.1%
ValueCountFrequency (%)
99.1875 1
< 0.1%
95.59375 1
< 0.1%
91.09375 1
< 0.1%
89.6875 1
< 0.1%
88.390625 1
< 0.1%
87.5 1
< 0.1%
85.09375 1
< 0.1%
84.390625 1
< 0.1%
83.6875 1
< 0.1%
82.390625 1
< 0.1%

aps
Real number (ℝ)

HIGH CORRELATION 

Distinct124
Distinct (%)1.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean37.614241
Minimum0
Maximum143
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:43.838814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q123
median34
Q349
95-th percentile76
Maximum143
Range143
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.974274
Coefficient of variation (CV)0.53102956
Kurtosis1.0766472
Mean37.614241
Median Absolute Deviation (MAD)12
Skewness0.95427806
Sum291059
Variance398.97163
MonotonicityNot monotonic
2024-09-24T10:41:43.882429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 201
 
2.6%
34 192
 
2.5%
31 181
 
2.3%
36 175
 
2.3%
22 174
 
2.2%
23 172
 
2.2%
35 171
 
2.2%
26 170
 
2.2%
20 169
 
2.2%
30 168
 
2.2%
Other values (114) 5965
77.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 7
 
0.1%
3 7
 
0.1%
4 5
 
0.1%
5 40
0.5%
6 39
0.5%
7 23
 
0.3%
8 31
 
0.4%
9 85
1.1%
ValueCountFrequency (%)
143 1
 
< 0.1%
133 1
 
< 0.1%
129 2
< 0.1%
127 1
 
< 0.1%
123 1
 
< 0.1%
121 2
< 0.1%
118 2
< 0.1%
117 1
 
< 0.1%
116 3
< 0.1%
115 2
< 0.1%

surv2m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct938
Distinct (%)12.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.6348917
Minimum0
Maximum0.9699707
Zeros120
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:43.929424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07598877
Q10.50488281
median0.71594238
Q30.8269043
95-th percentile0.90698242
Maximum0.9699707
Range0.9699707
Interquartile range (IQR)0.32202149

Descriptive statistics

Standard deviation0.24911854
Coefficient of variation (CV)0.39237958
Kurtosis0.12139622
Mean0.6348917
Median Absolute Deviation (MAD)0.13598633
Skewness-1.0325568
Sum4912.792
Variance0.062060046
MonotonicityNot monotonic
2024-09-24T10:41:43.974522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120
 
1.6%
0.827880859 31
 
0.4%
0.850952148 30
 
0.4%
0.863891602 29
 
0.4%
0.843994141 29
 
0.4%
0.819946289 29
 
0.4%
0.873901367 29
 
0.4%
0.823974609 29
 
0.4%
0.844970703 28
 
0.4%
0.860961914 27
 
0.3%
Other values (928) 7357
95.1%
ValueCountFrequency (%)
0 120
1.6%
0.000999928 19
 
0.2%
0.001999855 6
 
0.1%
0.002999783 15
 
0.2%
0.00399971 7
 
0.1%
0.004999161 2
 
< 0.1%
0.005999565 4
 
0.1%
0.00699997 9
 
0.1%
0.00799942 5
 
0.1%
0.008998871 5
 
0.1%
ValueCountFrequency (%)
0.969970703 1
 
< 0.1%
0.966918945 1
 
< 0.1%
0.963989258 1
 
< 0.1%
0.962890625 1
 
< 0.1%
0.9609375 1
 
< 0.1%
0.957885742 1
 
< 0.1%
0.95690918 2
 
< 0.1%
0.955932617 1
 
< 0.1%
0.954956055 5
0.1%
0.953979492 3
< 0.1%

surv6m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct931
Distinct (%)12.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.51896038
Minimum0
Maximum0.94799805
Zeros174
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:44.019171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.021999359
Q10.33795166
median0.57299805
Q30.72692871
95-th percentile0.85095215
Maximum0.94799805
Range0.94799805
Interquartile range (IQR)0.38897705

Descriptive statistics

Standard deviation0.25449922
Coefficient of variation (CV)0.49040203
Kurtosis-0.79421739
Mean0.51896038
Median Absolute Deviation (MAD)0.17895508
Skewness-0.54189592
Sum4015.7154
Variance0.064769854
MonotonicityNot monotonic
2024-09-24T10:41:44.063587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 174
 
2.2%
0.000999928 28
 
0.4%
0.642944336 25
 
0.3%
0.743896484 24
 
0.3%
0.708984375 23
 
0.3%
0.718994141 23
 
0.3%
0.693969727 22
 
0.3%
0.762939453 22
 
0.3%
0.00399971 21
 
0.3%
0.66796875 21
 
0.3%
Other values (921) 7355
95.0%
ValueCountFrequency (%)
0 174
2.2%
0.000999928 28
 
0.4%
0.001999855 11
 
0.1%
0.002999783 19
 
0.2%
0.00399971 21
 
0.3%
0.004999161 15
 
0.2%
0.005999565 12
 
0.2%
0.00699997 11
 
0.1%
0.00799942 7
 
0.1%
0.008998871 4
 
0.1%
ValueCountFrequency (%)
0.947998047 1
 
< 0.1%
0.942993164 1
 
< 0.1%
0.937988281 1
 
< 0.1%
0.936889648 3
< 0.1%
0.935913086 1
 
< 0.1%
0.932983398 2
< 0.1%
0.928955078 1
 
< 0.1%
0.926879883 2
< 0.1%
0.923950195 1
 
< 0.1%
0.922973633 3
< 0.1%

hday
Real number (ℝ)

Distinct83
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3826076
Minimum1
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:44.107814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile19
Maximum148
Range147
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.0483331
Coefficient of variation (CV)2.0646003
Kurtosis50.029374
Mean4.3826076
Median Absolute Deviation (MAD)0
Skewness5.7181948
Sum33917
Variance81.872331
MonotonicityNot monotonic
2024-09-24T10:41:44.151417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5059
65.4%
2 428
 
5.5%
3 340
 
4.4%
4 269
 
3.5%
5 183
 
2.4%
6 150
 
1.9%
7 141
 
1.8%
8 125
 
1.6%
9 92
 
1.2%
10 87
 
1.1%
Other values (73) 865
 
11.2%
ValueCountFrequency (%)
1 5059
65.4%
2 428
 
5.5%
3 340
 
4.4%
4 269
 
3.5%
5 183
 
2.4%
6 150
 
1.9%
7 141
 
1.8%
8 125
 
1.6%
9 92
 
1.2%
10 87
 
1.1%
ValueCountFrequency (%)
148 1
< 0.1%
140 1
< 0.1%
137 1
< 0.1%
128 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
95 2
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
88 1
< 0.1%

diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.8 KiB
0
6215 
1
1524 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7739
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6215
80.3%
1 1524
 
19.7%

Length

2024-09-24T10:41:44.192683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T10:41:44.229653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6215
80.3%
1 1524
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 6215
80.3%
1 1524
 
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6215
80.3%
1 1524
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6215
80.3%
1 1524
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6215
80.3%
1 1524
 
19.7%

dementia
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.8 KiB
0
7484 
1
 
255

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7739
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7484
96.7%
1 255
 
3.3%

Length

2024-09-24T10:41:44.260781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T10:41:44.297185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7484
96.7%
1 255
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 7484
96.7%
1 255
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7484
96.7%
1 255
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7484
96.7%
1 255
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7484
96.7%
1 255
 
3.3%

ca
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size777.9 KiB
no
5082 
metastatic
1593 
yes
1064 

Length

Max length10
Median length2
Mean length3.7842098
Min length2

Characters and Unicode

Total characters29286
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmetastatic
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 5082
65.7%
metastatic 1593
 
20.6%
yes 1064
 
13.7%

Length

2024-09-24T10:41:44.331021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T10:41:44.373779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 5082
65.7%
metastatic 1593
 
20.6%
yes 1064
 
13.7%

Most occurring characters

ValueCountFrequency (%)
n 5082
17.4%
o 5082
17.4%
t 4779
16.3%
a 3186
10.9%
e 2657
9.1%
s 2657
9.1%
m 1593
 
5.4%
i 1593
 
5.4%
c 1593
 
5.4%
y 1064
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 5082
17.4%
o 5082
17.4%
t 4779
16.3%
a 3186
10.9%
e 2657
9.1%
s 2657
9.1%
m 1593
 
5.4%
i 1593
 
5.4%
c 1593
 
5.4%
y 1064
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 5082
17.4%
o 5082
17.4%
t 4779
16.3%
a 3186
10.9%
e 2657
9.1%
s 2657
9.1%
m 1593
 
5.4%
i 1593
 
5.4%
c 1593
 
5.4%
y 1064
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 5082
17.4%
o 5082
17.4%
t 4779
16.3%
a 3186
10.9%
e 2657
9.1%
s 2657
9.1%
m 1593
 
5.4%
i 1593
 
5.4%
c 1593
 
5.4%
y 1064
 
3.6%

dnr
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing28
Missing (%)0.4%
Memory size815.2 KiB
no dnr
4997 
dnr after sadm
2513 
dnr before sadm
 
201

Length

Max length15
Median length6
Mean length8.8417845
Min length6

Characters and Unicode

Total characters68179
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdnr after sadm
2nd rowno dnr
3rd rowno dnr
4th rowno dnr
5th rowno dnr

Common Values

ValueCountFrequency (%)
no dnr 4997
64.6%
dnr after sadm 2513
32.5%
dnr before sadm 201
 
2.6%
(Missing) 28
 
0.4%

Length

2024-09-24T10:41:44.408289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T10:41:44.446291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dnr 7711
42.5%
no 4997
27.6%
sadm 2714
 
15.0%
after 2513
 
13.9%
before 201
 
1.1%

Most occurring characters

ValueCountFrequency (%)
n 12708
18.6%
10425
15.3%
d 10425
15.3%
r 10425
15.3%
a 5227
7.7%
o 5198
7.6%
e 2915
 
4.3%
f 2714
 
4.0%
s 2714
 
4.0%
m 2714
 
4.0%
Other values (2) 2714
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68179
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 12708
18.6%
10425
15.3%
d 10425
15.3%
r 10425
15.3%
a 5227
7.7%
o 5198
7.6%
e 2915
 
4.3%
f 2714
 
4.0%
s 2714
 
4.0%
m 2714
 
4.0%
Other values (2) 2714
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68179
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 12708
18.6%
10425
15.3%
d 10425
15.3%
r 10425
15.3%
a 5227
7.7%
o 5198
7.6%
e 2915
 
4.3%
f 2714
 
4.0%
s 2714
 
4.0%
m 2714
 
4.0%
Other values (2) 2714
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68179
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 12708
18.6%
10425
15.3%
d 10425
15.3%
r 10425
15.3%
a 5227
7.7%
o 5198
7.6%
e 2915
 
4.3%
f 2714
 
4.0%
s 2714
 
4.0%
m 2714
 
4.0%
Other values (2) 2714
 
4.0%

dnrday
Real number (ℝ)

Distinct172
Distinct (%)2.2%
Missing28
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean14.6007
Minimum-88
Maximum285
Zeros32
Zeros (%)0.4%
Negative169
Negative (%)2.2%
Memory size379.0 KiB
2024-09-24T10:41:44.486374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-88
5-th percentile1
Q14
median9
Q317
95-th percentile48
Maximum285
Range373
Interquartile range (IQR)13

Descriptive statistics

Standard deviation20.065423
Coefficient of variation (CV)1.3742781
Kurtosis29.165312
Mean14.6007
Median Absolute Deviation (MAD)5
Skewness4.2488923
Sum112586
Variance402.62121
MonotonicityNot monotonic
2024-09-24T10:41:44.532117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 544
 
7.0%
4 534
 
6.9%
1 511
 
6.6%
6 496
 
6.4%
7 433
 
5.6%
3 421
 
5.4%
9 361
 
4.7%
2 353
 
4.6%
8 345
 
4.5%
10 293
 
3.8%
Other values (162) 3420
44.2%
ValueCountFrequency (%)
-88 1
 
< 0.1%
-39 1
 
< 0.1%
-27 1
 
< 0.1%
-17 1
 
< 0.1%
-15 1
 
< 0.1%
-13 1
 
< 0.1%
-12 1
 
< 0.1%
-11 1
 
< 0.1%
-10 1
 
< 0.1%
-9 3
< 0.1%
ValueCountFrequency (%)
285 1
< 0.1%
281 1
< 0.1%
245 1
< 0.1%
226 1
< 0.1%
223 1
< 0.1%
211 1
< 0.1%
207 1
< 0.1%
202 1
< 0.1%
189 1
< 0.1%
186 1
< 0.1%

meanbp
Real number (ℝ)

Distinct159
Distinct (%)2.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean84.577216
Minimum0
Maximum195
Zeros45
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:44.580407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q163
median77
Q3107
95-th percentile130
Maximum195
Range195
Interquartile range (IQR)44

Descriptive statistics

Standard deviation27.687773
Coefficient of variation (CV)0.32736681
Kurtosis-0.29174
Mean84.577216
Median Absolute Deviation (MAD)20
Skewness0.24968069
Sum654458.5
Variance766.61278
MonotonicityNot monotonic
2024-09-24T10:41:44.625617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 248
 
3.2%
73 237
 
3.1%
67 205
 
2.6%
77 188
 
2.4%
65 185
 
2.4%
103 168
 
2.2%
107 167
 
2.2%
70 166
 
2.1%
68 151
 
2.0%
60 145
 
1.9%
Other values (149) 5878
76.0%
ValueCountFrequency (%)
0 45
0.6%
5 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
18 1
 
< 0.1%
22 2
 
< 0.1%
23 2
 
< 0.1%
24 2
 
< 0.1%
ValueCountFrequency (%)
195 1
 
< 0.1%
193 1
 
< 0.1%
187 1
 
< 0.1%
180 1
 
< 0.1%
178 1
 
< 0.1%
173 1
 
< 0.1%
172 2
< 0.1%
167 3
< 0.1%
166 1
 
< 0.1%
165 3
< 0.1%

hrt
Real number (ℝ)

Distinct184
Distinct (%)2.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean97.006293
Minimum0
Maximum300
Zeros72
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:44.673766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55
Q172
median100
Q3120
95-th percentile146
Maximum300
Range300
Interquartile range (IQR)48

Descriptive statistics

Standard deviation31.529275
Coefficient of variation (CV)0.32502298
Kurtosis0.64383123
Mean97.006293
Median Absolute Deviation (MAD)24
Skewness0.21944775
Sum750634.7
Variance994.09516
MonotonicityNot monotonic
2024-09-24T10:41:44.717537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 393
 
5.1%
70 318
 
4.1%
120 306
 
4.0%
110 301
 
3.9%
100 256
 
3.3%
60 238
 
3.1%
115 221
 
2.9%
125 174
 
2.2%
105 169
 
2.2%
65 164
 
2.1%
Other values (174) 5198
67.2%
ValueCountFrequency (%)
0 72
0.9%
5 1
 
< 0.1%
5.299805 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
11.398438 1
 
< 0.1%
14 1
 
< 0.1%
20 3
 
< 0.1%
22 2
 
< 0.1%
24 1
 
< 0.1%
ValueCountFrequency (%)
300 1
 
< 0.1%
250 1
 
< 0.1%
232 2
< 0.1%
230 1
 
< 0.1%
225 1
 
< 0.1%
224 1
 
< 0.1%
220 1
 
< 0.1%
215 2
< 0.1%
212 2
< 0.1%
210 3
< 0.1%

resp
Real number (ℝ)

Distinct64
Distinct (%)0.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.322176
Minimum0
Maximum90
Zeros56
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:44.764466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q118
median24
Q328
95-th percentile40
Maximum90
Range90
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.5531534
Coefficient of variation (CV)0.40961672
Kurtosis1.080763
Mean23.322176
Median Absolute Deviation (MAD)4
Skewness0.47656584
Sum180467
Variance91.262739
MonotonicityNot monotonic
2024-09-24T10:41:44.811807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 1173
15.2%
24 1055
13.6%
28 616
 
8.0%
22 451
 
5.8%
10 418
 
5.4%
16 367
 
4.7%
32 354
 
4.6%
12 332
 
4.3%
26 328
 
4.2%
30 285
 
3.7%
Other values (54) 2359
30.5%
ValueCountFrequency (%)
0 56
 
0.7%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 17
 
0.2%
5 9
 
0.1%
6 65
 
0.8%
7 19
 
0.2%
8 260
3.4%
9 56
 
0.7%
10 418
5.4%
ValueCountFrequency (%)
90 1
 
< 0.1%
76 2
 
< 0.1%
72 1
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
64 1
 
< 0.1%
60 13
0.2%
58 2
 
< 0.1%
56 7
0.1%
55 4
 
0.1%

temp
Real number (ℝ)

Distinct95
Distinct (%)1.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean37.108519
Minimum32
Maximum41.29688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:44.856612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile35.39844
Q136.19531
median36.69531
Q338.19531
95-th percentile39.19531
Maximum41.29688
Range9.29688
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2578108
Coefficient of variation (CV)0.033895473
Kurtosis-0.50500122
Mean37.108519
Median Absolute Deviation (MAD)0.89843
Skewness0.30399969
Sum287145.72
Variance1.582088
MonotonicityNot monotonic
2024-09-24T10:41:44.899752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.09375 418
 
5.4%
36.19531 397
 
5.1%
36.39844 383
 
4.9%
36 364
 
4.7%
36.29688 326
 
4.2%
36.59375 318
 
4.1%
36.5 278
 
3.6%
36.69531 268
 
3.5%
36.79688 265
 
3.4%
38 221
 
2.9%
Other values (85) 4500
58.1%
ValueCountFrequency (%)
32 1
< 0.1%
32.09375 1
< 0.1%
32.19531 1
< 0.1%
32.29688 1
< 0.1%
32.39844 1
< 0.1%
32.5 1
< 0.1%
32.89844 2
< 0.1%
33 1
< 0.1%
33.09375 1
< 0.1%
33.19531 1
< 0.1%
ValueCountFrequency (%)
41.29688 1
 
< 0.1%
41.19531 2
< 0.1%
41.09375 1
 
< 0.1%
41 3
< 0.1%
40.89844 2
< 0.1%
40.79688 1
 
< 0.1%
40.69531 2
< 0.1%
40.59375 3
< 0.1%
40.5 4
0.1%
40.39844 3
< 0.1%

crea
Real number (ℝ)

Distinct126
Distinct (%)1.6%
Missing55
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.7701601
Minimum0.09999084
Maximum21.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:44.943750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.09999084
5-th percentile0.59997559
Q10.89990234
median1.1999512
Q31.8999023
95-th percentile5.3994141
Maximum21.5
Range21.400009
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.68865
Coefficient of variation (CV)0.95395326
Kurtosis14.876342
Mean1.7701601
Median Absolute Deviation (MAD)0.40002441
Skewness3.263518
Sum13601.91
Variance2.8515387
MonotonicityNot monotonic
2024-09-24T10:41:44.985515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.89990234 685
 
8.9%
0.79992676 662
 
8.6%
0.69995117 555
 
7.2%
1 546
 
7.1%
1.09985352 534
 
6.9%
1.19995117 521
 
6.7%
1.29980469 401
 
5.2%
0.59997559 354
 
4.6%
1.39990234 313
 
4.0%
1.5 271
 
3.5%
Other values (116) 2842
36.7%
ValueCountFrequency (%)
0.09999084 1
 
< 0.1%
0.19998169 5
 
0.1%
0.29998779 16
 
0.2%
0.39996338 64
 
0.8%
0.5 164
 
2.1%
0.59997559 354
4.6%
0.69995117 555
7.2%
0.79992676 662
8.6%
0.89990234 685
8.9%
1 546
7.1%
ValueCountFrequency (%)
21.5 1
 
< 0.1%
18.3984375 2
< 0.1%
14.6992188 1
 
< 0.1%
14.5 1
 
< 0.1%
14 1
 
< 0.1%
13.7988281 1
 
< 0.1%
13.1992188 1
 
< 0.1%
13.0996094 1
 
< 0.1%
12.8984375 2
< 0.1%
12.3984375 3
< 0.1%

sod
Real number (ℝ)

Distinct59
Distinct (%)0.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean137.56694
Minimum110
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:45.028749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile128
Q1134
median137
Q3141
95-th percentile148
Maximum181
Range71
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.059353
Coefficient of variation (CV)0.044046577
Kurtosis1.6878062
Mean137.56694
Median Absolute Deviation (MAD)4
Skewness0.35295508
Sum1064493
Variance36.715759
MonotonicityNot monotonic
2024-09-24T10:41:45.073221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136 628
 
8.1%
137 582
 
7.5%
138 569
 
7.4%
135 527
 
6.8%
139 503
 
6.5%
134 466
 
6.0%
140 455
 
5.9%
141 421
 
5.4%
142 409
 
5.3%
133 393
 
5.1%
Other values (49) 2785
36.0%
ValueCountFrequency (%)
110 1
 
< 0.1%
111 1
 
< 0.1%
112 2
 
< 0.1%
113 1
 
< 0.1%
114 1
 
< 0.1%
115 1
 
< 0.1%
116 1
 
< 0.1%
117 4
0.1%
118 4
0.1%
119 6
0.1%
ValueCountFrequency (%)
181 1
 
< 0.1%
175 1
 
< 0.1%
173 1
 
< 0.1%
168 2
 
< 0.1%
166 1
 
< 0.1%
165 1
 
< 0.1%
164 3
< 0.1%
163 1
 
< 0.1%
161 2
 
< 0.1%
160 7
0.1%

adlsc
Real number (ℝ)

ZEROS 

Distinct1519
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8830174
Minimum0
Maximum7
Zeros2649
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size379.0 KiB
2024-09-24T10:41:45.120137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0024989
Coefficient of variation (CV)1.0634522
Kurtosis-0.0518187
Mean1.8830174
Median Absolute Deviation (MAD)1
Skewness0.94353723
Sum14572.671
Variance4.010002
MonotonicityNot monotonic
2024-09-24T10:41:45.163561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2649
34.2%
1 877
 
11.3%
2 399
 
5.2%
6 348
 
4.5%
3 276
 
3.6%
5 276
 
3.6%
7 255
 
3.3%
4 217
 
2.8%
0.494751 210
 
2.7%
0.4947999 163
 
2.1%
Other values (1509) 2069
26.7%
ValueCountFrequency (%)
0 2649
34.2%
0.494751 210
 
2.7%
0.4947999 163
 
2.1%
1 877
 
11.3%
1.0517578 1
 
< 0.1%
1.112793 1
 
< 0.1%
1.1416016 1
 
< 0.1%
1.1667481 75
 
1.0%
1.1668997 62
 
0.8%
1.3105469 1
 
< 0.1%
ValueCountFrequency (%)
7 255
3.3%
6.9287109 1
 
< 0.1%
6.7988281 1
 
< 0.1%
6.5625 1
 
< 0.1%
6.5097656 1
 
< 0.1%
6.4658203 1
 
< 0.1%
6.4472656 1
 
< 0.1%
6.3632812 1
 
< 0.1%
6.3398438 1
 
< 0.1%
6.2919922 1
 
< 0.1%

hospdead
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.8 KiB
0
5747 
1
1992 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7739
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5747
74.3%
1 1992
 
25.7%

Length

2024-09-24T10:41:45.204298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T10:41:45.240718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5747
74.3%
1 1992
 
25.7%

Most occurring characters

ValueCountFrequency (%)
0 5747
74.3%
1 1992
 
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5747
74.3%
1 1992
 
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5747
74.3%
1 1992
 
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5747
74.3%
1 1992
 
25.7%

Interactions

2024-09-24T10:41:41.843464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:31.317312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.064589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.667139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.289018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.892247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:34.696633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:35.332367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:35.957973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:36.574793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:37.217798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:37.870488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:38.749008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:39.376300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:39.979258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:40.590570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.191461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.878519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:31.381606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.098402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.702127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.321992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.925442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:34.731954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:35.367452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:35.993422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:36.610642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:37.253824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:37.907442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:38.784658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:39.409758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:40.013102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:40.623884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.227643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.914164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:31.435031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.137161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.736358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.355894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.959323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:34.768059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:35.401895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:36.028686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:36.646440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:37.290547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:37.944037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:38.820419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:39.442511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:40.046046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:40.657915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.264202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.950358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:31.526330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.171425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.770608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.390288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.993643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:34.804160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:35.438448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:36.063874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:36.682419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:37.327892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:38.209209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:38.857534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-09-24T10:41:37.789645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:38.668343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:39.299265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:39.905038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:40.514937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.117131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.760605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:42.445535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.027050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:32.630069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.251053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:33.855579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:34.658501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:35.292425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:35.919675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:36.537830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:37.178246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:37.830425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:38.709262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:39.338259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:39.942277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:40.552881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.154889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-24T10:41:41.799989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-09-24T10:41:45.279164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
adlscageapsavtisstcacreadementiadiabetesdnrdnrdaydzclassdzgrouphdayhospdeadhrtmeanbpnum.coracerespscomasexsodspssurv2msurv6mtemp
adlsc1.0000.1290.1050.0040.0520.0060.2950.0900.154-0.1480.0940.070-0.0180.1530.018-0.0370.1550.0230.0160.1380.090-0.0000.095-0.150-0.144-0.004
age0.1291.0000.024-0.1190.1400.0780.2620.1350.181-0.2190.1440.132-0.0790.061-0.134-0.0390.1170.1080.0050.0500.0880.037-0.107-0.218-0.225-0.077
aps0.1050.0241.0000.5760.2910.2680.0350.1260.2090.1500.3490.2350.3680.4800.169-0.1670.0310.0160.1360.2780.0100.0170.782-0.593-0.4870.078
avtisst0.004-0.1190.5761.0000.2230.1030.0390.0080.2040.3320.3670.2420.4940.5520.184-0.123-0.1850.0220.0250.3450.0210.0430.581-0.466-0.3420.180
ca0.0520.1400.2910.2231.0000.0710.0790.1270.0830.1120.6090.6940.1100.1300.0480.0750.2610.0510.0670.1190.0220.0190.2140.1770.2450.049
crea0.0060.0780.2680.1030.0711.0000.0080.0690.0440.0230.0930.0620.0710.139-0.029-0.1530.0770.010-0.0170.0560.0170.0570.155-0.122-0.097-0.065
dementia0.2950.2620.0350.0390.0790.0081.0000.0350.1290.0130.0870.1090.0050.0120.0330.0280.1360.0230.0000.0930.0300.0070.0350.0740.0580.000
diabetes0.0900.1350.1260.0080.1270.0690.0351.0000.0130.0000.1230.1970.0250.0080.0600.0190.3940.0570.0560.0010.0400.0000.0580.0300.0620.000
dnr0.1540.1810.2090.2040.0830.0440.1290.0131.0000.1460.1700.2110.0740.5730.0480.0680.0460.0460.0630.2070.0570.0660.2000.3280.3100.053
dnrday-0.148-0.2190.1500.3320.1120.0230.0130.0000.1461.0000.1580.1110.2640.0330.0930.006-0.1400.000-0.004-0.0190.0090.0030.1570.0580.1330.111
dzclass0.0940.1440.3490.3670.6090.0930.0870.1230.1700.1581.0001.0000.1310.3570.0960.1160.3070.0390.1150.3830.0600.0700.3010.2980.2550.140
dzgroup0.0700.1320.2350.2420.6940.0620.1090.1970.2110.1111.0001.0000.0920.3820.0710.0820.2180.0580.0810.2510.0910.0620.2060.2380.2160.100
hday-0.018-0.0790.3680.4940.1100.0710.0050.0250.0740.2640.1310.0921.0000.1750.124-0.032-0.1750.0200.0310.2500.0000.0060.376-0.376-0.2780.140
hospdead0.1530.0610.4800.5520.1300.1390.0120.0080.5730.0330.3570.3820.1751.0000.1540.1480.0820.0320.1400.3720.0000.0920.4580.5540.4990.114
hrt0.018-0.1340.1690.1840.048-0.0290.0330.0600.0480.0930.0960.0710.1240.1541.0000.012-0.0700.0350.1960.0650.0070.0170.214-0.137-0.1180.263
meanbp-0.037-0.039-0.167-0.1230.075-0.1530.0280.0190.0680.0060.1160.082-0.0320.1480.0121.000-0.0320.0320.034-0.0150.0270.061-0.1690.1260.116-0.019
num.co0.1550.1170.031-0.1850.2610.0770.1360.3940.046-0.1400.3070.218-0.1750.082-0.070-0.0321.0000.0190.013-0.1160.059-0.036-0.0550.1020.081-0.118
race0.0230.1080.0160.0220.0510.0100.0230.0570.0460.0000.0390.0580.0200.0320.0350.0320.0191.0000.0110.0380.0520.0180.0340.0360.0470.022
resp0.0160.0050.1360.0250.067-0.0170.0000.0560.063-0.0040.1150.0810.0310.1400.1960.0340.0130.0111.0000.0160.0160.0250.087-0.055-0.0440.060
scoma0.1380.0500.2780.3450.1190.0560.0930.0010.207-0.0190.3830.2510.2500.3720.065-0.015-0.1160.0380.0161.0000.0350.0700.252-0.510-0.4450.097
sex0.0900.0880.0100.0210.0220.0170.0300.0400.0570.0090.0600.0910.0000.0000.0070.0270.0590.0520.0160.0351.0000.0000.0250.0210.0210.043
sod-0.0000.0370.0170.0430.0190.0570.0070.0000.0660.0030.0700.0620.0060.0920.0170.061-0.0360.0180.0250.0700.0001.000-0.033-0.0070.0050.092
sps0.095-0.1070.7820.5810.2140.1550.0350.0580.2000.1570.3010.2060.3760.4580.214-0.169-0.0550.0340.0870.2520.025-0.0331.000-0.740-0.6510.072
surv2m-0.150-0.218-0.593-0.4660.177-0.1220.0740.0300.3280.0580.2980.238-0.3760.554-0.1370.1260.1020.036-0.055-0.5100.021-0.007-0.7401.0000.969-0.064
surv6m-0.144-0.225-0.487-0.3420.245-0.0970.0580.0620.3100.1330.2550.216-0.2780.499-0.1180.1160.0810.047-0.044-0.4450.0210.005-0.6510.9691.000-0.034
temp-0.004-0.0770.0780.1800.049-0.0650.0000.0000.0530.1110.1400.1000.1400.1140.263-0.019-0.1180.0220.0600.0970.0430.0920.072-0.064-0.0341.000

Missing values

2024-09-24T10:41:42.512010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-24T10:41:42.905941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-24T10:41:43.005418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agesexdzgroupdzclassnum.coscomaavtisstracespsapssurv2msurv6mhdaydiabetesdementiacadnrdnrdaymeanbphrtresptempcreasodadlschospdead
651974.29999maleMOSF w/MaligARF/MOSF19.042.500000white33.00000062.00.2279970.128998800metastaticdnr after sadm1.0109.0117.08.038.000003.899902136.01.0000001
117447.30499maleARF/MOSF w/SepsisARF/MOSF4100.050.000000white41.29687583.00.0249980.0060002200nono dnr5.062.0145.032.039.195312.500000145.04.6552731
68358.69699femaleCHFCOPD/CHF/Cirrhosis30.04.000000hispanic19.39843840.00.8839110.806885110nono dnr3.0100.080.028.036.593752.199707138.02.3715820
539056.31198maleCirrhosisCOPD/CHF/Cirrhosis19.018.000000white22.39843828.00.7479250.602905100nono dnr15.076.0100.024.037.500001.299805139.00.0000000
764569.96899maleCOPDCOPD/CHF/Cirrhosis29.013.000000white20.69921924.00.8129880.696899100nono dnr4.070.0106.040.038.093750.599976134.03.8188480
9975.76196maleARF/MOSF w/SepsisARF/MOSF00.023.666656white22.69921928.00.7349850.652954800nono dnr21.068.079.020.037.195311.099854132.00.4948000
684777.85596femaleCHFCOPD/CHF/Cirrhosis20.023.000000black15.29882819.00.8029790.681885100metastaticdnr after sadm7.064.0120.037.039.093751.599854132.00.0000001
259976.08795maleARF/MOSF w/SepsisARF/MOSF10.027.333328black19.59765635.00.8299560.772949100nodnr after sadm7.047.060.042.036.093750.299988141.00.0000000
453762.86398maleCHFCOPD/CHF/Cirrhosis40.07.000000white13.00000013.00.9229740.869995110noNaNNaN0.00.010.036.093751.099854139.00.0000000
156565.25397femaleComaComa144.032.666657white21.00000026.00.5069580.435974500nono dnr17.058.075.038.037.898440.799927141.00.0000000
agesexdzgroupdzclassnum.coscomaavtisstracespsapssurv2msurv6mhdaydiabetesdementiacadnrdnrdaymeanbphrtresptempcreasodadlschospdead
568645.64499maleLung CancerCancer10.06.500000white21.09765610.00.6699220.370972100metastaticno dnr7.048.0175.028.039.898440.899902142.00.4947510
344665.52496femaleComaComa0100.014.000000black22.59765634.00.2399900.173981100nodnr after sadm5.062.064.028.035.296881.500000136.02.3056641
287589.99896femaleARF/MOSF w/SepsisARF/MOSF00.036.500000white22.89843848.00.7288820.644897100nodnr after sadm1.042.076.08.036.593751.500000138.05.0000001
836451.87698maleCHFCOPD/CHF/Cirrhosis20.013.666664white19.59765633.00.8979490.828979100nono dnr15.079.098.024.035.593751.699951135.00.0000000
523754.33798maleCirrhosisCOPD/CHF/Cirrhosis20.016.000000black24.19921931.00.7569580.614990100nono dnr11.079.049.024.036.500001.000000139.03.0000000
16753.86697maleCHFCOPD/CHF/Cirrhosis20.06.000000white14.19921925.00.9239500.871948200nono dnr4.063.068.026.036.000001.299805139.00.4948000
483225.25400femaleARF/MOSF w/SepsisARF/MOSF00.045.500000black45.50000071.00.5229490.406982300nodnr after sadm24.0122.0125.024.036.195311.399902141.00.0000000
783271.13794femaleCHFCOPD/CHF/Cirrhosis20.07.500000white23.00000045.00.8039550.683960100nono dnr10.061.064.028.035.898442.500000137.00.0000000
228378.60394femaleARF/MOSF w/SepsisARF/MOSF10.024.666656white21.29687531.00.7559810.678955610nono dnr21.0120.068.08.038.093753.299805145.00.0000000
879941.88898femaleLung CancerCancer10.04.000000white16.50000011.00.7679440.518921100metastaticno dnr5.070.062.020.037.695310.899902138.01.0000000